(1) Field of Invention
The present invention relates to a system for multi-layered object detection and, more particularly, to a system for multi-layered object detection which utilizes particle swarm optimization and hierarchical representation schemes.
(2) Description of Related Art
In typical object recognition systems, a classifier is used exhaustively either at all potential locations of a scene or at locations cued by other sensors such as radar-based, LIDAR, or stereo sensors. For an exhaustive search case, the objects are processed in the entire field-of-view using the same parameter sets and classifiers. This limitation burdens the system to find the most suitable choice of parameters across the entire field-of-view.
Particle swarm optimization (PSO) is a relatively new optimization method with unique attributes making it especially suitable for feature selection and optimization. An advantage of PSO is the simple and natural way that candidate solutions are represented as points in a separable space. This feature allows almost any feature to be represented and provides great flexibility in specifying regions of the solution space to be explored. Another advantage is PSO's superior performance due to its good balancing of cooperation and competition, which arises from the use of swarm intelligence principles.
PSO is a simple but powerful population-based algorithm that is effective for optimization of a wide range of functions as described by Kennedy et al. in “Swarm Intelligence”, San Francisco: Morgan Kaufmann Publishers, 2001, and by Eberhart and Shi in “Particle Swarm Optimization: Developments, Applications, and Resources”, 2001, which is incorporated by reference as though fully set forth herein.
PSO models the exploration of a multi-dimensional solution space by a “swarm” of software agents, or particles, where the success of each agent has an influence on the dynamics of other members of the swarm. Each particle in the swarm resides in a multi-dimensional solution space. The positions of the particles represent candidate problem solutions. Additionally, each particle has a velocity vector that allows it to explore the space in search of an objective function optima. Each particle i keeps track of a position vector {right arrow over (y)}i that represents the current best solution the particle has found. Another position vector {right arrow over (y)}g is used to store the current global best solution found by all of the particles. The velocity and position vectors for particle i are then changed probabilistically according to the following set of dynamic update equations:{right arrow over (vi)}(t+1)=w{right arrow over (vi)}(t)+c1q1[{right arrow over (yi)}(t)]+c2q2[{right arrow over (yg)}(t)−{right arrow over (xi)}(t)]{right arrow over (xi)}(t+1)={right arrow over (xi)}(t)+X{right arrow over (vi)}(t+1),where {right arrow over (xi)}(t) and {right arrow over (vi)}(t) are the position and velocity vectors at time t of the i-th particle and c1 and c2 are parameters that weight the influence of the “individual best” {right arrow over (y)} and “swarm best” {right arrow over (y)}g terms. w is a momentum constant that prevents premature convergence, and x is a constriction factor which also influences the convergence of PSO. q1 and q2 are random variables that allow the particles to better explore the solution space. The described dynamics cause the swarm to concentrate on promising regions of solution space very quickly with sparse sampling of the solution space.